Landslide　susceptibility　mapping　(LSM)　could　be　an　effective　way　to　prevent　landslide　hazards　and　mitigate　losses.　The　choice　of　conditional　factors　is　crucial　to　the　results　of　LSM,　and　the　selection　of　models　also　plays　an　important　role.　In　this　study,　a　hybrid　method　including　GeoDetector　and　machine　learning　cluster　was　developed　to　provide　a　new　perspective　on　how　to　address　these　two　issues.　We　defined　redundant　factors　by　quantitatively　analyzing　the　single　impact　and　interactive　impact　of　the　factors,　which　was　analyzed　by　GeoDetector,　the　effect　of　this　step　was　examined　using　mean　absolute　error　(MAE).　The　machine　learning　cluster　contains　four　models　(artificial　neural　network　(ANN),　Bayesian　network　(BN),　logistic　regression　(LR),　and　support　vector　machines　(SVM))　and　automatically　selects　the　best　one　for　generating　LSM.　The　receiver　operating　characteristic　(ROC)　curve,　prediction　accuracy,　and　the　seed　cell　area　index　(SCAI)　methods　were　used　to　evaluate　these　methods.　The　results　show　that　the　SVM　model　had　the　best　performance　in　the　machine　learning　cluster　with　the　area　under　the　ROC　curve　of　0.928　and　with　an　accuracy　of　83.86%.　Therefore,　SVM　was　chosen　as　the　assessment　model　to　map　the　landslide　susceptibility　of　the　study　area.　The　landslide　susceptibility　map　demonstrated　fit　with　landslide　inventory,　indicated　the　hybrid　method　is　effective　in　screening　landslide　influences　and　assessing　landslide　susceptibility.